↓ Skip to main content

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq

Overview of attention for article published in BMC Genomics, July 2015
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
1 blog
twitter
84 X users
patent
2 patents

Citations

dimensions_citation
143 Dimensions

Readers on

mendeley
473 Mendeley
citeulike
5 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq
Published in
BMC Genomics, July 2015
DOI 10.1186/s12864-015-1767-y
Pubmed ID
Authors

Anto P. Rajkumar, Per Qvist, Ross Lazarus, Francesco Lescai, Jia Ju, Mette Nyegaard, Ole Mors, Anders D. Børglum, Qibin Li, Jane H. Christensen

Abstract

Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples. False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values. Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

X Demographics

X Demographics

The data shown below were collected from the profiles of 84 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 473 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 4 <1%
United Kingdom 3 <1%
Germany 2 <1%
Portugal 2 <1%
United States 2 <1%
Norway 1 <1%
Sweden 1 <1%
Finland 1 <1%
India 1 <1%
Other 6 1%
Unknown 450 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 114 24%
Researcher 97 21%
Student > Master 72 15%
Student > Bachelor 42 9%
Student > Doctoral Student 32 7%
Other 53 11%
Unknown 63 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 177 37%
Biochemistry, Genetics and Molecular Biology 126 27%
Medicine and Dentistry 18 4%
Immunology and Microbiology 13 3%
Neuroscience 13 3%
Other 44 9%
Unknown 82 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 57. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 December 2020.
All research outputs
#713,710
of 24,792,414 outputs
Outputs from BMC Genomics
#85
of 11,067 outputs
Outputs of similar age
#8,541
of 268,622 outputs
Outputs of similar age from BMC Genomics
#4
of 265 outputs
Altmetric has tracked 24,792,414 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,067 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,622 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 265 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.